Title: Investigation of principal factor decision support system using data mining methodology for surface grinding wheel

Authors: Hiroyuki Kodama; Takao Mendori; Kazuhito Ohashi

Addresses: Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan ' Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan ' Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Kita-ku, Okayama, 700-8530, Japan

Abstract: The five factors (abrasive grain, grain size, grade, structure and bonding material) of the three main elements (abrasive grain, bonding material and pore) of a grinding wheel are important parameters affecting surface quality and grinding efficiency, however it is difficult to determine an optimal combination of grinding conditions for workpiece material. In previous research, we constructed a support system for effectively selecting an appropriate grinding wheel using decision tree technique. We also proposed a visualisation process to show how grinding wheel elements and factors correspond to the materials characteristics of the workpiece material. In this research, to evaluate the usefulness of prepared visualisation maps and their effectiveness in deciding grinding wheel elements, we performed comparison experiments applying the surface grinding technique to JIS SUS310S material using PA abrasive grain as recommended by the grain-type visualisation map and WA and GC abrasive grains for comparison purposes. We found that visualisation maps enable quick selection of a grinding wheel even for the grinding of difficult-to-cut materials for which grinding wheel selection is usually difficult.

Keywords: data mining; supervised learning; decision tree; grinding wheel; surface grinding.

DOI: 10.1504/IJAT.2019.106676

International Journal of Abrasive Technology, 2019 Vol.9 No.4, pp.303 - 318

Received: 09 Apr 2019
Accepted: 19 Dec 2019
Published online: 16 Apr 2020 *

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